Yue Wang

Computer Vision and Pattern Recognition · with Lin · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 180 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 123 citations into it; #21,058 of 21,575 by trust here, #12,689 by citations
• Artificial Intelligence — 96 citations into it; #34,609 of 35,345 by trust here, #19,018 by citations
• Efficiency & Compression — 83 citations into it
• Model Architectures — 34 citations into it
• Optimization & Training — 18 citations into it
• Electrical and Electronic Engineering — 17 citations into it

Whom Yue Wang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Ilya Sutskever
6. Yoshua Bengio
7. Yann LeCun
8. Yu-hsin Chen
9. X. Zhang
10. Christian Szegedy

Who trusts Yue Wang overall

1. Ting-Kuei Hu
2. Tianlong Chen
3. Zhangyang Wang
4. Jianghao Shen
5. Bowen Pan
6. Youwei Liang
7. Breton L. Minnehan
8. Yonggan Fu
9. Haotao Wang
10. Xavier Suau

Papers from Yue Wang's perspective

Ordered by how strongly the researchers Yue Wang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference (2019) — 93 citations
• Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference (2020) — 49 citations
• Drawing early-bird tickets: Towards more efficient training of deep networks (2019) — 285 citations
• EnergyNet: Energy-Efficient Dynamic Inference (2018) — 21 citations
• Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions (2018) — 121 citations